{"title":"评估自动驾驶车辆-行人交互的安全性:一种极值理论方法","authors":"Abdul Razak Alozi, Mohamed Hussein","doi":"10.1016/j.amar.2022.100230","DOIUrl":null,"url":null,"abstract":"<div><p>With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3–3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.5000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Evaluating the safety of autonomous vehicle–pedestrian interactions: An extreme value theory approach\",\"authors\":\"Abdul Razak Alozi, Mohamed Hussein\",\"doi\":\"10.1016/j.amar.2022.100230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3–3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.</p></div>\",\"PeriodicalId\":47520,\"journal\":{\"name\":\"Analytic Methods in Accident Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analytic Methods in Accident Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213665722000197\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytic Methods in Accident Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213665722000197","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Evaluating the safety of autonomous vehicle–pedestrian interactions: An extreme value theory approach
With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3–3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.
期刊介绍:
Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.